docstr: Performs power series approximation to the vector product of x with the
  matrix exponential of M.
docstr_tok: ["Performs","power","series","approximation","to","the","vector","product","of","x","with","the","matrix","exponential","of","M","."]
code: def _exp(self, x, M):
      """
      Performs power series approximation to the vector product of x with the
      matrix exponential of M.
      """
      power_term = x.unsqueeze(-1)
      y = x.unsqueeze(-1)
      for idx in range(self.iterations):
          power_term = torch.matmul(M, power_term) / (idx + 1)
          y = y + power_term
  
      return y.squeeze(-1)
  
imports: import math
  from functools import partial
  import torch
  import torch.nn
  from torch.distributions import Transform, constraints
  from pyro.nn import DenseNN
  from ..conditional import ConditionalTransformModule
  from ..torch_transform import TransformModule
  from ..util import copy_docs_from
label_desc: Use image node_id 2 for calling the ConditionedMatrixExponential obj's underlying member method code with example usage: obj._exp(x, M) and returns: y